Populating LinkedIn Profile Content for AI Personalization

Solution

Use the Enrich Person from Profile with Companies People Jobs integration to retrieve comprehensive profile details, including work experience from profile URLs.

Accessing Experience Data

1. Reference the retrieved data using the **[dynamic data referencing guide](https://www.clay.com/university/guide/referencing-dynamic-data-from-other-columns?c354c4e8_page=

2)** 2. Expand the first item **(Index

0)** to access specific details such as:

• Company name
• Role title
• Summary information

Why Content May Not Populate

Profile fields may not be visible if they require an active login session to view. Since Clay's data providers do not use active sessions to scrape live data, certain information protected by LinkedIn's login requirements will not be retrievable—even with valid profile URLs.

LinkedIn Profile Data Structure Issue

The Problem: Experience data returns as structured JSON objects where each role is a separate object. Asking AI to "look at the profile" doesn't work because it treats unstructured JSON as plain text.

The Solution:

1. [Reference dynamic data from other columns](https://www.clay.com/university/guide/referencing-dynamic-data-from-other-columns?c354c4e8_page=

2. within the experience list

2. Use formulas to extract and format relevant experience data (company name, role title, summary, etc.) from individual experience objects
3. Feed the structured data into your AI prompt for reliable personalization

This approach converts unstructured JSON into formatted, readable data that AI can effectively use for cold email personalization.

Why LinkedIn Profile Content Isn't Populating for AI Personalization

AI personalization in cold emails relies on two main data sources from LinkedIn profiles:

1. LinkedIn Summary

• Clay cannot generate personalization without a summary field
• If the profile's summary section is blank, there's no meaningful data for the model to work with
• Action: Ensure the target profile has a completed LinkedIn summary

2. Experience Data

• Clay returns experience as a JSON array, with each role as a separate object
• Feeding raw experience data directly to the AI treats it as unstructured text, resulting in unreliable or "weird" results
• Solution: Use formulas to extract and format relevant data from each experience item before feeding it into your prompt
• This structured approach ensures reliable personalization

Best Practice

Extract and format LinkedIn data using formulas before passing it to the AI, rather than asking the model to interpret raw profile information directly.